Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity

To develop a novel method for predicting neonatal respiratory morbidity (NRM) by ultrasound-based radiomics technology. In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 295 fetal lung ultrasound images (four-chamber view) in 295 single pregnancies. Images had been obtained between 28+3 and 37+6 weeks of gestation within 72 h before delivery. A machine-learning model built by RUSBoost (Random under-sampling with AdaBoost) architecture was created using 20 radiomics features extracted from the images and 2 clinical features (gestational age and pregnancy complications) to predict the possibility of NRM. Of the 295 standard fetal lung ultrasound images included, 210 in the training set and 85 in the testing set. The overall performance of the neonatal respiratory morbidity prediction model achieved AUC of 0.88 (95% CI 0.83–0.92) in the training set and 0.83 (95% CI 0.79–0.97) in the testing set, sensitivity of 84.31% (95% CI 79.06–89.44%) in the training set and 77.78% (95% CI 68.30–87.43%) in the testing set, specificity of 81.13% (95% CI 78.16–84.07%) in the training set and 82.09% (95% CI 77.65–86.62%) in the testing set, and accuracy of 81.90% (95% CI 79.34–84.41%) in the training set and 81.18% (95% CI 77.33–85.12%) in the testing set. Ultrasound-based radiomics technology can be used to predict NRM. The results of this study may provide a novel method for non-invasive approaches for the prenatal prediction of NRM.

Neonatal respiratory morbidity (NRM), associated with prematurity, is the leading cause of mortality and morbidity 1 . Fetal lung maturity (FLM) was influenced by many factors, including gestational diabetes mellitus (GDM) and pre-eclampsia (PE), the two most common complications of pregnancy 2,3 . With the increasing use of assisted reproductive technology (ART), the incidence of gestational hypertension and GDM in these women is 11.0% and 15.1% respectively 4 . Accurate estimates of fetal lung development in pregnancies during complications will help obstetricians make clinical decisions that can avoid unnecessary premature birth and ensure optimal maternal and fetal outcomes. Although the methods and techniques have been improved since the L/S ratio was applied 25 years ago, FLM detection still cannot predict whether the fetal lung is mature or not 5 .
In recent years, the combination of ultrasound images with artificial intelligence technology has provided new ideas for the detection of FLM 6,7 . Radiomics is a technology that combines big data and medical imaging-assisted diagnosis. By extracting and mining high-throughput features from multi-modality images, it can quantitatively analyze the human molecular and genetic changes hidden behind medical images. This technology has been widely used in the analysis of ultrasound images [8][9][10] . But to the best of our knowledge, there is no published research on ultrasound-based radiomics technology being employed to study the development of fetal lungs during pregnancy complications.
In the present study, by collecting fetal lung ultrasound standard images, the fetal lung texture characteristics were analyzed and compared using ultrasound-based radiomics technology. A neonatal respiratory morbidity prediction model was established by using the ultrasound image features of fetal lungs combined with clinical Neonatal respiratory morbidity prediction model. By permuting out-of-bag data feature of random regression forest, 20 radiomics features and 2 clinical features (GA and Pregnancy complications) were selected and input into RUSBoost classifier to predict the possibility of NRM. Calibration, gain and lift curves created with the cross-validation results to see how much the predictive model would have helped to predict possibility of NRM are shown in Fig. 1. The confusion matrix and model performance for predicting neonatal respiratory morbidity depending on different features (clinical features, radiomics features and the combination of clinical and radiomics features) are shown in Table 2

Discussion
The results of the present study revealed that fetal lung texture analysis by ultrasound-based radiomics technology can be used to predict the probability of neonatal respiratory morbidity by analyzing fetal lung ultrasound images and in combination with clinical characteristics (GA and pregnancy complications). It may provide a new method for noninvasive prediction of NRM.
The clinical utility of FLM assays has been largely debated 11 . At present, instead of studying several components of the amniotic fluid through amniocentesis, the application of prenatal corticoids and postnatal surfactant has become the main clinical measure to reduce neonatal respiratory diseases 12 . However, the recommended type of corticosteroid and the gestational window of treatment administration have not been clearly defined 13 . Studies have shown that there are potentially important risks of corticosteroids in neurodevelopment and fetal metabolic planning [14][15][16] . In a study of 278,508 live-born singletons of 24 weeks gestation or above in Finland, antenatal steroid was shown to be associated with the delivery of small fetus at birth 17 . The results of this study may provide a new method for non-invasive approaches for the prenatal assessment of FLM, which can not only avoid the fear and discomfort of amniocentesis, help to decide whether to use prenatal corticosteroids, but also refine the timing of delivery in high-risk pregnancies.
With the widespread use of ultrasound in obstetrics, several attempts have been made to evaluate fetal lung maturity noninvasively. Sm et al. 18 showed that a measured elevated acceleration-to-ejection time ratio of the fetal pulmonary artery doppler was independently associated with the development of RDS in preterm infants and thus a possible marker of lung maturity. Attempts to quantify fetal lung volume in normal pregnancies by using 3-dimensional ultrasonography though useful in cases like diaphragmatic hernia have not been shown to objectively evaluate FLM 19,20 . In addition, gray scale measurement 21 , fetal lung tissue movement assessment 22 , and evaluation of fetal lung images relative to fetal liver and fetal placenta images 23 have been tried to proposed as a possible tool for the assessment of fetal lung maturity. Unfortunately, the accuracy of this diagnosis is very poor, so no clinical significance is found. Recently, Palacio et al. 24 reported that the quantitative ultrasound lung texture analysis could be used to evaluate fetal lung maturity and showed an accuracy similar to that of biochemical tests   25 reported that there were great differences in fetal lung texture between pregnancies with GDM, PE and normal pregnancy and between different gestational ages. In our study population, there were 33.2% (98/295) of pregnant women with GDM and PE. Among these, the proportion of newborns with NRM was nearly twice that in the normal pregnancy group (6.1% vs 3.2%). Therefore, in this study, the model  Our study had several limitations: First, large amounts of data are necessary in radiomics for mining concealed prognostic information and to avoid overfitting. Expanding the sample size, especially the positive sample size, would improve the stability and accuracy of the model. Second, in this study, the ROIs of fetal lungs were performed manually. A computer system will be used to identify fetal lung tissue automatically, so that the model could be used more conveniently. Third, it is a single-center study, and image acquisition and delineation were performed by highly-trained personnel. But as the number of operators and settings increases, there will be many unqualified images. Multi-center research will be carried out in the future.
In conclusion, ultrasound-based radiomics technology can be used to predict neonatal respiratory morbidity. The results of this study may provide a new method for non-invasive approaches for the prenatal prediction of NRM.

Methods
Patients. Between July 2018 and October 2020, 2047 routine fetal-lung ultrasound images (either right or left lung) from 2047 women with singleton pregnancy were obtained, at gestational ages (GA) ranging from 27 +3 to 42 +0 weeks. All participating women included in the study gave written informed consent for the use of ultrasound images and clinical data. All the methods hereby explained were performed in accordance with the relevant guidelines and regulations and approved, together with the study protocol, by the ethics committee of the Obstetrics and Gynecology Hospital Affiliated to Fudan University (2018-73). Of these, 731 babies with GA 28 +3 -37 +6 weeks were delivered within 72 h after ultrasound examination in the hospital. According to the same enrolment criteria of previous studies, the final cohort comprised 295 women with singleton pregnancy, with a total of 295 fetal-lung ultrasound images. The flowchart for the study population is shown in Fig. 3. Gestational age was determined by last menstrual period and verified by first-trimester dating ultrasound (crown-rump length).
Pregnancy complications included GDM and PE. GDM was diagnosed using a 75-g oral glucose tolerance test at 24-28 weeks of gestation 27 . Pre-eclampsia and gestational hypertension are characterized by the new onset of hypertension (> 140 mmHg systolic or > 90 mmHg diastolic) after 20 weeks gestation 28 .
Analysis of neonatal clinical data was supervised by a neonatal doctor. NRM included respiratory distress syndrome (RDS) or transient tachypnea of the newborn (TTN). The diagnosis of RDS and TTN is based on symptoms, signs and radiological examination 7,29 . Diagnostic criteria of RDS: tachypnea, snoring, chest wall retraction, nasal dilatation, the need for supplemental oxygen and the appearance of chest X-rays led to admission to the neonatal intensive care unit for respiratory support. Diagnostic criteria of TTN: mild or moderate respiratory distress (isolated tachypnea, rare snoring, slight retraction) and a chest X-ray (if done) showing alveolar and/or pulmonary interstitial effusion and prominent pulmonary vascular patterns. www.nature.com/scientificreports/ Ultrasound imaging and segmentation. All ultrasound images were obtained during routine prenatal ultrasound examinations within 72 h before delivery. Among which, the images of the training set were obtained by radiologist 1 with more than 10 years' experience in obstetric and gynecological ultrasound imaging, using aWS80A ultrasound system (Samsung, Korea). The frequency of the CA1-7A probe was 1-7 MHz, with a center frequency was 4.0 MHz. The images of the testing set were obtained by radiologist 2 with 3 years' experience in obstetric and gynecological ultrasound imaging, using a VOLUSON E8 ultrasound system (GE, United States) . The frequency of the C1-5-D probe was 2-5 MHz, with a center frequency was 3.5 MHz. A detailed description of the standard image acquisition protocol and the method used of manual (free-hand) delineation is fully described in a previous study 25 : Briefly, the standard fetal lung images requiring: on an axial section of the fetal thorax at the level of the four-chamber cardiac view, the settings were adjusted (depth, gain, frequency and harmonics) to ensure that at least one of the lungs had no obvious acoustic shadowing from the fetal ribs. All the images were inspected for image quality control and stored in DICOM format (.dcm) for offline analysis. Manual (free-hand) delineation was performed in each fetal lung by two radiologists (radiologists A and B), and square delineation (40 × 40 pixels) was performed by radiologist B, selecting one side of the fetal lung, taking great care to ensure that only the lung tissue was delineated, and avoiding blood vessels, rib shadows, and the lung capsule, as shown in Fig. 4. The radiologist A's segmentation results were used to generate the model, while the radiologist B's segmentation and the square delineation results were utilized to verify the stability of the model. Fig. 5.

Radiomics evaluation and machine learning. The research process is shown in
All the feature extraction and image classifications were carried out using Matlab R2018a and Toolbox Classification (Mathworks, Inc, Natick, Massachusetts, US).
Univariate analysis was used to describe the differences in features among the different categories. The t-test was performed on each 430 continuous radiomics features 25 , including 15 morphological, 73 texture and 342 wavelet features. The χ 2 test was performed for two categorical clinical features, gestational age and pregnancy complications. P value < 0.05 indicated a significant difference.
The feature extraction method to analyze each ROI has been previously reported 25 . First, high-throughput radiomics features importance per fetal lung image were ranked to selected features by permuting out-of-bag data feature of random regression forest. If a feature is influential, permuting its values would influence the model error testing with out-of-bag data. The more important a feature is, the greater its influence will be 30 . As a result, 20 radiomics features (2 texture features and 18 wavelet features) and 2 clinical features (GA and Pregnancy complications) were selected to classification, which are shown in Table 4. The stability of selected radiomic features depending on different delineations (manual delineation by radiologists A and B and square delineation) was analyzed with ICC (2, 1) 31 . Then, the diagnostic performance of predicting neonatal respiratory morbidity depending on different features was compared, including clinical features (GA and pregnancy complications), radiomics features and the combination of clinical and radiomics features. For clinical features,   32 was used to build the model. Finally, the risk probability of NRM in each fetal lung image was obtained, which was the predicted score normalized to the range of 0-1 by softmax function of the RUSBoost. The cut-off point of the model was 0.5. The fetal lungs with risk probability higher than 0.5 were divided into the high-risk group, and lower than 0.5 were divided into the low-risk group. All classifier parameters were tuned with bootstrap tenfold cross-validation, and the decision tree was employed as the base learner for RUSBoost. The prediction performance of the model was assessed for sensitivity (SENS), specificity (SPEC), accuracy, PPV, NPV and AUC.

Data availability
The data that support the findings of this study are available at the web repository of https:// pan. baidu. com/s/ 1p9ka t4pr3 jFrE1 jPE8O 5wA and its extraction code can be obtained from the corresponding author upon a separate request.  Table 4. List of high-throughput sonographic features. Between the normal group and the NRM group, T-test was performed for each feature. P value < 0.05 indicated a significant difference. The smaller the P value, the greater the probability that the feature is significantly different between the two groups. The stability of selected radiomic features depending on different delineations (manual delineation by radiologists A and B and square delineation) was analyzed with ICC (2, 1). GA gestational age, GDM gestational diabetes mellitus, PE preeclampsia, ICC intraclass correlation coefficient. www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.